Pneumonia image classification based on convolutional neural network

Feng Xiong; Di He; Yujie Liu; Meijie Qi; Zhoufeng Zhang; Lixin Liu

doi:10.1117/12.2606413

1 November 2021 Pneumonia image classification based on convolutional neural network

Feng Xiong, Di He, Yujie Liu, Meijie Qi, Zhoufeng Zhang, Lixin Liu

Proceedings Volume 12057, Twelfth International Conference on Information Optics and Photonics; 120573C (2021) https://doi.org/10.1117/12.2606413
Event: Twelfth International Conference on Information Optics and Photonics, 2021, Xi'an, China

Abstract

Chest X-ray is the commonly used method to diagnose pneumonia. How to correctly interpret the image information is always the main challenge faced by doctors. Convolution Neural Network (CNN) is a popular deep learning algorithm with excellent image recognition performance, and has been used widely in automatic recognition and diagnosis of medical images. This paper studies the classification of normal and pneumonia with more than 5000 chest X-ray images by employing three CNN models of VGG16, VGG19 and Inception_V3. The performances of each model for classification was evaluated and compared.

Citation Download Citation

Feng Xiong, Di He, Yujie Liu, Meijie Qi, Zhoufeng Zhang, and Lixin Liu "Pneumonia image classification based on convolutional neural network", Proc. SPIE 12057, Twelfth International Conference on Information Optics and Photonics, 120573C (1 November 2021); https://doi.org/10.1117/12.2606413

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available